Reconstructing Land Use History from Landsat Time-Series

Gepubliceerd op

11 januari 2016

PhD student Loïc Dutrieux of the Laboratory of Geo-information Science and Remote Sensing just won the Outstanding Student Paper Award for his presentation on the annual meeting of the American Geophysical Union (AGU). “This is a big achievement since 25.000 people are attending this conference,” Martin Herold says.

The Outstanding Student Paper Awards (OSPAs) are awarded to promote, recognize and reward students for quality research in the geophysical sciences. It is a great honor for young scientists at the beginning of their careers. Typically the top 3-5% of presenters in each section or focus group are awarded an OSPA. Loïc Dutrieux got the award for the presentation he gave at the AGU meeting about his publication ‘Reconstructing Land Use History from Landsat Time-Series: Case study of a swidden agriculture system in Brazil’, in collaboration with the Forest Ecology and Management group of Wageningen University.

Loïc Dutrieux published his rewarded research in the International Journal of Applied Earth Observation and Geo-information. “We developed a method to reconstruct land use history from Landsat images time-series,” he says. Secondary forests largely contribute to harbouring biodiversity, provision of ecosystem services or climate change mitigation. However, their capacity to deliver these services also depends on how intensively the land on which they grow has been used in the past. “Thanks to the research of Catarina Jacovac we knew that land use intensity has an impact on the capacity of the forests to regenerate, but there were no ways to get spatially continuous information on past land use dynamics. We therefore decided to investigate how remote sensing could help us with that.”

The method was developed using open source tools, some of which were developed by Loïc and his colleagues from the Geo-Information Science and Remote Sensing group. “In our research we used a breakpoint detection framework derived from the econometrics field which we applied to the remote sensing time-series. This allows us to partition a time-series into the multiple land dynamic regimes it has gone though in the past 30 years. And this information is very valuable, particularly for ecologists working on land use dynamics and the impacts they have on secondary forests.”